| Autor: |
Richard de Latour, Thomas, Chenouard, Raphaël, Granvilliers, Laurent |
| Zdroj: |
International Journal on Interactive Design & Manufacturing; May2024, Vol. 18 Issue 4, p2291-2307, 17p |
| Abstrakt: |
During preliminary phases in product design, on the basis of strong physical hypotheses (e.g. isotherm, steady state), physical and functional requirements can be expressed as coarse-grained constraint-based models on a few degrees of freedom, possibly including several design criteria to optimize. Such models are usually handled by multi-objective optimization solvers in order to find design solutions giving the best trade-offs between design criteria. Another approach developed in this paper is to partially explore all the areas of the design space using an anytime interval branch-and-prune algorithm called IDFS such that the design criteria are converted into so-called ε -constraints. The expected result is a sample of solutions diversified in both the objective space and the design space. Several quality indicators are introduced in order to measure this diversity and compare IDFS with two state-of-the-art multi-objective optimization solvers NSGA-II and NSGA-III on three real-world case studies. The results show that IDFS is able to identify new close-to-optimal designs and permits a better understanding of the design space. This framework provides a promising alternative tool for decision making, in particular for integrating interaction in the preliminary design process. Partial exploration aims to compute a diversified subset of feasible solutions; We built an anytime branch and prune algorithm for partial design space exploration. We built a protocol to analyze diversity in both the design and the objective space. We compare partial exploration and optimization approaches on three design problems. Partial Exploration is a tool for decision makers to identify quasi-optimal designs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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